from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    UpperCamelCase_    ):
  lowercase_								=  [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']




  @register_to_config
  def __init__(   self  :						str  ,			a_  :						int  ,			a_  :						int  ,			a_  :						Optional[int] = None  ,			a_  :						int = 5_0257  ,			a_  :						int = 1024  ,			a_  :						int = 768  ,			a_  :						int = 12  ,			a_  :						int = 12  ,			a_  :						Optional[int] = None  ,			a_  :						str = "gelu_new"  ,			a_  :						float = 0.1  ,			a_  :						float = 0.1  ,			a_  :						float = 0.1  ,			a_  :						float = 1e-5  ,			a_  :						float = 0.02  ,			a_  :						bool = True  ,			a_  :						bool = True  ,			a_  :						bool = False  ,			a_  :						bool = False  ,			)->							Optional[int]:




       """simple docstring"""
       super().__init__()

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		prefix_length

       if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
            raise ValueError(
                F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
                F''' `n_embd`: {n_embd} are not equal.'''					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		prefix_inner_dim
       SCREAMING_SNAKE_CASE__					:					str      =		prefix_hidden_dim

       SCREAMING_SNAKE_CASE__					:					str      =		(
           nn.Linear(self.prefix_inner_dim  ,			self.prefix_hidden_dim					)
           if self.prefix_hidden_dim is not None
           else nn.Identity()
       )
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		(
           nn.Linear(self.prefix_hidden_dim  ,			a_					) if self.prefix_hidden_dim is not None else nn.Identity()
       )

       SCREAMING_SNAKE_CASE__					:					List[str]      =		GPTaConfig(
           vocab_size=a_  ,			n_positions=a_  ,			n_embd=a_  ,			n_layer=a_  ,			n_head=a_  ,			n_inner=a_  ,			activation_function=a_  ,			resid_pdrop=a_  ,			embd_pdrop=a_  ,			attn_pdrop=a_  ,			layer_norm_epsilon=a_  ,			initializer_range=a_  ,			scale_attn_weights=a_  ,			use_cache=a_  ,			scale_attn_by_inverse_layer_idx=a_  ,			reorder_and_upcast_attn=a_  ,			)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		GPTaLMHeadModel(a_					)




  def 						__lowercase(   self  :						Any  ,			a_  :						torch.Tensor  ,			a_  :						torch.Tensor  ,			a_  :						Optional[torch.Tensor] = None  ,			a_  :						Optional[torch.Tensor] = None  ,			)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.transformer.transformer.wte(a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.encode_prefix(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.decode_prefix(a_					)
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.cat((prefix_embeds, embedding_text)  ,			dim=1					)

       if labels is not None:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_dummy_token(input_ids.shape[0]  ,			input_ids.device					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		torch.cat((dummy_token, input_ids)  ,			dim=1					)
       SCREAMING_SNAKE_CASE__					:					Any      =		self.transformer(inputs_embeds=a_  ,			labels=a_  ,			attention_mask=a_					)
       if self.prefix_hidden_dim is not None:
            return out, hidden
       else:
            return out




  def 						__lowercase(   self  :						str  ,			a_  :						int  ,			a_  :						torch.device					)->							torch.Tensor:




       """simple docstring"""
       return torch.zeros(a_  ,			self.prefix_length  ,			dtype=torch.intaa  ,			device=a_					)




  def 						__lowercase(   self  :						str  ,			a_  :						int					)->							Any:




       """simple docstring"""
       return self.encode_prefix(a_					)




  @torch.no_grad()
  def 						__lowercase(   self  :						List[Any]  ,			a_  :						Tuple  ,			a_  :						int  ,			a_  :						str					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.split(a_  ,			1  ,			dim=0					)
       SCREAMING_SNAKE_CASE__					:					int      =		[]
       SCREAMING_SNAKE_CASE__					:					Dict      =		[]
       for feature in features:
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.decode_prefix(feature.to(a_					)					)  # back to the clip feature
            # Only support beam search for now
            SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.generate_beam(
                input_embeds=a_  ,			device=a_  ,			eos_token_id=a_					)
            generated_tokens.append(output_tokens[0]					)
            generated_seq_lengths.append(seq_lengths[0]					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.stack(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.stack(a_					)
       return generated_tokens, generated_seq_lengths




  @torch.no_grad()
  def 						__lowercase(   self  :						Tuple  ,			a_  :						Optional[int]=None  ,			a_  :						Optional[Any]=None  ,			a_  :						str=None  ,			a_  :						int = 5  ,			a_  :						int = 67  ,			a_  :						float = 1.0  ,			a_  :						Optional[int] = None  ,			)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		eos_token_id
       SCREAMING_SNAKE_CASE__					:					Tuple      =		None
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		None
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.ones(a_  ,			device=a_  ,			dtype=torch.int					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.zeros(a_  ,			device=a_  ,			dtype=torch.bool					)

       if input_embeds is not None:
            SCREAMING_SNAKE_CASE__					:					Dict      =		input_embeds
       else:
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.transformer.transformer.wte(a_					)

       for i in range(a_					):
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.transformer(inputs_embeds=a_					)
            SCREAMING_SNAKE_CASE__					:					int      =		outputs.logits
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		logits.softmax(-1					).log()

            if scores is None:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		logits.topk(a_  ,			-1					)
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		generated.expand(a_  ,			*generated.shape[1:]					)
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		next_tokens.permute(1  ,			0					), scores.squeeze(0					)
                 if tokens is None:
                      SCREAMING_SNAKE_CASE__					:					Dict      =		next_tokens
                 else:
                      SCREAMING_SNAKE_CASE__					:					Dict      =		tokens.expand(a_  ,			*tokens.shape[1:]					)
                      SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.cat((tokens, next_tokens)  ,			dim=1					)
            else:
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		-float(np.inf					)
                 SCREAMING_SNAKE_CASE__					:					Any      =		0
                 SCREAMING_SNAKE_CASE__					:					Any      =		scores[:, None] + logits
                 seq_lengths[~is_stopped] += 1
                 SCREAMING_SNAKE_CASE__					:					Dict      =		scores_sum / seq_lengths[:, None]
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		scores_sum_average.view(-1					).topk(a_  ,			-1					)
                 SCREAMING_SNAKE_CASE__					:					str      =		next_tokens // scores_sum.shape[1]
                 SCREAMING_SNAKE_CASE__					:					str      =		seq_lengths[next_tokens_source]
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		next_tokens % scores_sum.shape[1]
                 SCREAMING_SNAKE_CASE__					:					Any      =		next_tokens.unsqueeze(1					)
                 SCREAMING_SNAKE_CASE__					:					int      =		tokens[next_tokens_source]
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.cat((tokens, next_tokens)  ,			dim=1					)
                 SCREAMING_SNAKE_CASE__					:					Any      =		generated[next_tokens_source]
                 SCREAMING_SNAKE_CASE__					:					List[str]      =		scores_sum_average * seq_lengths
                 SCREAMING_SNAKE_CASE__					:					int      =		is_stopped[next_tokens_source]

            SCREAMING_SNAKE_CASE__					:					int      =		self.transformer.transformer.wte(next_tokens.squeeze()					).view(generated.shape[0]  ,			1  ,			-1					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.cat((generated, next_token_embed)  ,			dim=1					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		is_stopped + next_tokens.eq(a_					).squeeze()
            if is_stopped.all():
                 break

       SCREAMING_SNAKE_CASE__					:					Dict      =		scores / seq_lengths
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		scores.argsort(descending=a_					)
       # tokens tensors are already padded to max_seq_length
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[tokens[i] for i in order]
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.stack(a_  ,			dim=0					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.tensor([seq_lengths[i] for i in order]  ,			dtype=seq_lengths.dtype					)
       return output_texts, seq_lengths







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_						:  Dict				  =						{
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						['MobileBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  Optional[Any]				  =						[
				    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileBertForMaskedLM',
				    'MobileBertForMultipleChoice',
				    'MobileBertForNextSentencePrediction',
				    'MobileBertForPreTraining',
				    'MobileBertForQuestionAnswering',
				    'MobileBertForSequenceClassification',
				    'MobileBertForTokenClassification',
				    'MobileBertLayer',
				    'MobileBertModel',
				    'MobileBertPreTrainedModel',
				    'load_tf_weights_in_mobilebert',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_						:  List[str]				  =						[
				    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileBertForMaskedLM',
				    'TFMobileBertForMultipleChoice',
				    'TFMobileBertForNextSentencePrediction',
				    'TFMobileBertForPreTraining',
				    'TFMobileBertForQuestionAnswering',
				    'TFMobileBertForSequenceClassification',
				    'TFMobileBertForTokenClassification',
				    'TFMobileBertMainLayer',
				    'TFMobileBertModel',
				    'TFMobileBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mobilebert import (
				    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileBertConfig,
				    MobileBertOnnxConfig,
				)
				from .tokenization_mobilebert import MobileBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mobilebert_fast import MobileBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilebert import (
								    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileBertForMaskedLM,
								    MobileBertForMultipleChoice,
								    MobileBertForNextSentencePrediction,
								    MobileBertForPreTraining,
								    MobileBertForQuestionAnswering,
								    MobileBertForSequenceClassification,
								    MobileBertForTokenClassification,
								    MobileBertLayer,
								    MobileBertModel,
								    MobileBertPreTrainedModel,
								    load_tf_weights_in_mobilebert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilebert import (
								    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileBertForMaskedLM,
								    TFMobileBertForMultipleChoice,
								    TFMobileBertForNextSentencePrediction,
								    TFMobileBertForPreTraining,
								    TFMobileBertForQuestionAnswering,
								    TFMobileBertForSequenceClassification,
								    TFMobileBertForTokenClassification,
								    TFMobileBertMainLayer,
								    TFMobileBertModel,
								    TFMobileBertPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_						:  str				  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




